{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "据美国约翰斯·霍普金斯大学的疫情统计数据显示，7月16日全天，全球和美国单日新增新冠肺炎确诊病例均创新高。全球新增新冠肺炎确诊病例24.98万例，美国新增新冠肺炎确诊病例7.73万例。\n",
    "\n",
    "截至北京时间7月17日14:10，全球累计确诊病例1380.86万例，累计死亡人数58.99万例。美国累计确诊357.62万例，巴西累计确诊201.22万例，印度累计确诊100.38万例。\n",
    "数据说明：\n",
    "全球covid19死亡数据.csv；全球covid19治愈数据.csv；全球covid19确诊数据.csv；美国covid19死亡数据.csv；美国covid19确诊数据.csv；全球总体.csv；疫情相关新闻.csv；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.figure_factory as ff\n",
    "from plotly import subplots\n",
    "from plotly.subplots import make_subplots\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "from datetime import datetime, date, timedelta\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# pd.set_option('display.max_columns', 100)\n",
    "# pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 找到收集的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['全球covid19死亡数据.csv',\n",
       " '全球covid19治愈数据.csv',\n",
       " '全球covid19确诊数据.csv',\n",
       " '全球总体.csv',\n",
       " '全球死亡数.csv',\n",
       " '全球治愈数.csv',\n",
       " '全球确诊.csv',\n",
       " '疫情相关新闻.csv',\n",
       " '美国covid19死亡数据.csv',\n",
       " '美国covid19确诊数据.csv']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "root = r'C:\\Users\\86187\\sumPandas\\期末 Python数据分析基础\\data\\新冠状病毒数据'\n",
    "file_names = os.listdir(root)\n",
    "file_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fn = { \"input\" : { \"df_全球死亡数\": \"全球covid19死亡数据.csv\",\\\n",
    "                  \"df_全球治愈数\": \"全球covid19治愈数据.csv\",\\\n",
    "                  \"df_全球确诊数\": \"全球covid19确诊数据.csv\",\\\n",
    "                  \"df_美国死亡数\": \"美国covid19死亡数据.csv\",\\\n",
    "                  \"df_美国确诊数\": \"美国covid19确诊数据.csv\",\\\n",
    "                  \"df_全球总体\": \"全球总体.csv\",\\\n",
    "                  \"df_新闻\": \"疫情相关新闻.csv\"\\\n",
    "                 } \\\n",
    "      }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_全球死亡数 = pd.read_csv(\"./data/新冠状病毒数据/全球covid19死亡数据.csv\")\n",
    "df_全球治愈数 = pd.read_csv(\"./data/新冠状病毒数据/全球covid19治愈数据.csv\")\n",
    "df_全球确诊数 = pd.read_csv(\"./data/新冠状病毒数据/全球covid19确诊数据.csv\")\n",
    "df_美国死亡数 = pd.read_csv(\"./data/新冠状病毒数据/美国covid19死亡数据.csv\")\n",
    "df_美国确诊数 = pd.read_csv(\"./data/新冠状病毒数据/美国covid19确诊数据.csv\")\n",
    "df_全球总体 = pd.read_csv(\"./data/新冠状病毒数据/全球总体.csv\")\n",
    "df_新闻 = pd.read_csv(\"./data/新冠状病毒数据/疫情相关新闻.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对比查看各类数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>2007/4/20</th>\n",
       "      <th>2007/5/20</th>\n",
       "      <th>2007/6/20</th>\n",
       "      <th>2007/7/20</th>\n",
       "      <th>2007/8/20</th>\n",
       "      <th>2007/9/20</th>\n",
       "      <th>7/13/20</th>\n",
       "      <th>7/14/20</th>\n",
       "      <th>7/15/20</th>\n",
       "      <th>7/16/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32672</td>\n",
       "      <td>32951</td>\n",
       "      <td>33190</td>\n",
       "      <td>33384</td>\n",
       "      <td>33594</td>\n",
       "      <td>33908</td>\n",
       "      <td>34455</td>\n",
       "      <td>34740</td>\n",
       "      <td>34994</td>\n",
       "      <td>35070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2819</td>\n",
       "      <td>2893</td>\n",
       "      <td>2964</td>\n",
       "      <td>3038</td>\n",
       "      <td>3106</td>\n",
       "      <td>3188</td>\n",
       "      <td>3571</td>\n",
       "      <td>3667</td>\n",
       "      <td>3752</td>\n",
       "      <td>3851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15500</td>\n",
       "      <td>15941</td>\n",
       "      <td>16404</td>\n",
       "      <td>16879</td>\n",
       "      <td>17348</td>\n",
       "      <td>17808</td>\n",
       "      <td>19689</td>\n",
       "      <td>20216</td>\n",
       "      <td>20770</td>\n",
       "      <td>21355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>855</td>\n",
       "      <td>858</td>\n",
       "      <td>861</td>\n",
       "      <td>862</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>346</td>\n",
       "      <td>346</td>\n",
       "      <td>346</td>\n",
       "      <td>386</td>\n",
       "      <td>386</td>\n",
       "      <td>396</td>\n",
       "      <td>525</td>\n",
       "      <td>541</td>\n",
       "      <td>576</td>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>719</td>\n",
       "      <td>720</td>\n",
       "      <td>721</td>\n",
       "      <td>724</td>\n",
       "      <td>724</td>\n",
       "      <td>726</td>\n",
       "      <td>732</td>\n",
       "      <td>732</td>\n",
       "      <td>737</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1248</td>\n",
       "      <td>1265</td>\n",
       "      <td>1284</td>\n",
       "      <td>1297</td>\n",
       "      <td>1318</td>\n",
       "      <td>1356</td>\n",
       "      <td>1498</td>\n",
       "      <td>1516</td>\n",
       "      <td>1526</td>\n",
       "      <td>1552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>309</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>313</td>\n",
       "      <td>314</td>\n",
       "      <td>317</td>\n",
       "      <td>321</td>\n",
       "      <td>321</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6159</td>\n",
       "      <td>6213</td>\n",
       "      <td>6262</td>\n",
       "      <td>6315</td>\n",
       "      <td>6364</td>\n",
       "      <td>6410</td>\n",
       "      <td>6596</td>\n",
       "      <td>6643</td>\n",
       "      <td>6695</td>\n",
       "      <td>6741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>79</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>134</td>\n",
       "      <td>245</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
       "0              NaN            Afghanistan  33.939110  67.709953        0   \n",
       "1              NaN                Albania  41.153300  20.168300        0   \n",
       "2              NaN                Algeria  28.033900   1.659600        0   \n",
       "3              NaN                Andorra  42.506300   1.521800        0   \n",
       "4              NaN                 Angola -11.202700  17.873900        0   \n",
       "..             ...                    ...        ...        ...      ...   \n",
       "261            NaN  Sao Tome and Principe   0.186400   6.613100        0   \n",
       "262            NaN                  Yemen  15.552727  48.516388        0   \n",
       "263            NaN                Comoros -11.645500  43.333300        0   \n",
       "264            NaN             Tajikistan  38.861000  71.276100        0   \n",
       "265            NaN                Lesotho -29.610000  28.233600        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  2007/4/20  2007/5/20  \\\n",
       "0          0        0        0        0        0  ...      32672      32951   \n",
       "1          0        0        0        0        0  ...       2819       2893   \n",
       "2          0        0        0        0        0  ...      15500      15941   \n",
       "3          0        0        0        0        0  ...        855        855   \n",
       "4          0        0        0        0        0  ...        346        346   \n",
       "..       ...      ...      ...      ...      ...  ...        ...        ...   \n",
       "261        0        0        0        0        0  ...        719        720   \n",
       "262        0        0        0        0        0  ...       1248       1265   \n",
       "263        0        0        0        0        0  ...        309        311   \n",
       "264        0        0        0        0        0  ...       6159       6213   \n",
       "265        0        0        0        0        0  ...         35         79   \n",
       "\n",
       "     2007/6/20  2007/7/20  2007/8/20  2007/9/20  7/13/20  7/14/20  7/15/20  \\\n",
       "0        33190      33384      33594      33908    34455    34740    34994   \n",
       "1         2964       3038       3106       3188     3571     3667     3752   \n",
       "2        16404      16879      17348      17808    19689    20216    20770   \n",
       "3          855        855        855        855      858      861      862   \n",
       "4          346        386        386        396      525      541      576   \n",
       "..         ...        ...        ...        ...      ...      ...      ...   \n",
       "261        721        724        724        726      732      732      737   \n",
       "262       1284       1297       1318       1356     1498     1516     1526   \n",
       "263        311        311        313        314      317      321      321   \n",
       "264       6262       6315       6364       6410     6596     6643     6695   \n",
       "265         91         91         91        134      245      256      256   \n",
       "\n",
       "     7/16/20  \n",
       "0      35070  \n",
       "1       3851  \n",
       "2      21355  \n",
       "3        877  \n",
       "4        607  \n",
       "..       ...  \n",
       "261      740  \n",
       "262     1552  \n",
       "263      328  \n",
       "264     6741  \n",
       "265      256  \n",
       "\n",
       "[266 rows x 163 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_全球确诊数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>7/7/20</th>\n",
       "      <th>7/8/20</th>\n",
       "      <th>7/9/20</th>\n",
       "      <th>7/10/20</th>\n",
       "      <th>7/11/20</th>\n",
       "      <th>7/12/20</th>\n",
       "      <th>7/13/20</th>\n",
       "      <th>7/14/20</th>\n",
       "      <th>7/15/20</th>\n",
       "      <th>7/16/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>920</td>\n",
       "      <td>936</td>\n",
       "      <td>957</td>\n",
       "      <td>971</td>\n",
       "      <td>994</td>\n",
       "      <td>1010</td>\n",
       "      <td>1012</td>\n",
       "      <td>1048</td>\n",
       "      <td>1094</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>81</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>89</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>97</td>\n",
       "      <td>101</td>\n",
       "      <td>104</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>968</td>\n",
       "      <td>978</td>\n",
       "      <td>988</td>\n",
       "      <td>996</td>\n",
       "      <td>1004</td>\n",
       "      <td>1011</td>\n",
       "      <td>1018</td>\n",
       "      <td>1028</td>\n",
       "      <td>1040</td>\n",
       "      <td>1052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>348</td>\n",
       "      <td>351</td>\n",
       "      <td>361</td>\n",
       "      <td>364</td>\n",
       "      <td>365</td>\n",
       "      <td>417</td>\n",
       "      <td>424</td>\n",
       "      <td>429</td>\n",
       "      <td>433</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
       "0              NaN            Afghanistan  33.939110  67.709953        0   \n",
       "1              NaN                Albania  41.153300  20.168300        0   \n",
       "2              NaN                Algeria  28.033900   1.659600        0   \n",
       "3              NaN                Andorra  42.506300   1.521800        0   \n",
       "4              NaN                 Angola -11.202700  17.873900        0   \n",
       "..             ...                    ...        ...        ...      ...   \n",
       "261            NaN  Sao Tome and Principe   0.186400   6.613100        0   \n",
       "262            NaN                  Yemen  15.552727  48.516388        0   \n",
       "263            NaN                Comoros -11.645500  43.333300        0   \n",
       "264            NaN             Tajikistan  38.861000  71.276100        0   \n",
       "265            NaN                Lesotho -29.610000  28.233600        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  7/7/20  7/8/20  7/9/20  \\\n",
       "0          0        0        0        0        0  ...     920     936     957   \n",
       "1          0        0        0        0        0  ...      81      83      83   \n",
       "2          0        0        0        0        0  ...     968     978     988   \n",
       "3          0        0        0        0        0  ...      52      52      52   \n",
       "4          0        0        0        0        0  ...      21      21      22   \n",
       "..       ...      ...      ...      ...      ...  ...     ...     ...     ...   \n",
       "261        0        0        0        0        0  ...      13      13      14   \n",
       "262        0        0        0        0        0  ...     348     351     361   \n",
       "263        0        0        0        0        0  ...       7       7       7   \n",
       "264        0        0        0        0        0  ...      53      54      54   \n",
       "265        0        0        0        0        0  ...       0       0       1   \n",
       "\n",
       "     7/10/20  7/11/20  7/12/20  7/13/20  7/14/20  7/15/20  7/16/20  \n",
       "0        971      994     1010     1012     1048     1094     1113  \n",
       "1         85       89       93       95       97      101      104  \n",
       "2        996     1004     1011     1018     1028     1040     1052  \n",
       "3         52       52       52       52       52       52       52  \n",
       "4         23       23       26       26       26       27       28  \n",
       "..       ...      ...      ...      ...      ...      ...      ...  \n",
       "261       14       14       14       14       14       14       14  \n",
       "262      364      365      417      424      429      433      438  \n",
       "263        7        7        7        7        7        7        7  \n",
       "264       55       55       55       55       56       56       56  \n",
       "265        1        1        2        3        3        3        3  \n",
       "\n",
       "[266 rows x 181 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_全球死亡数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>267</td>\n",
       "      <td>268</td>\n",
       "      <td>269</td>\n",
       "      <td>279</td>\n",
       "      <td>283</td>\n",
       "      <td>283</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>322</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>537</td>\n",
       "      <td>552</td>\n",
       "      <td>575</td>\n",
       "      <td>591</td>\n",
       "      <td>595</td>\n",
       "      <td>619</td>\n",
       "      <td>675</td>\n",
       "      <td>685</td>\n",
       "      <td>694</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>241</td>\n",
       "      <td>266</td>\n",
       "      <td>266</td>\n",
       "      <td>266</td>\n",
       "      <td>272</td>\n",
       "      <td>272</td>\n",
       "      <td>296</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4809</td>\n",
       "      <td>4858</td>\n",
       "      <td>4914</td>\n",
       "      <td>4965</td>\n",
       "      <td>5011</td>\n",
       "      <td>5067</td>\n",
       "      <td>5278</td>\n",
       "      <td>5332</td>\n",
       "      <td>5383</td>\n",
       "      <td>5431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State         Country/Region        Lat       Long  1/22/20  \\\n",
       "0              NaN            Afghanistan  33.939110  67.709953        0   \n",
       "1              NaN                Albania  41.153300  20.168300        0   \n",
       "2              NaN                Algeria  28.033900   1.659600        0   \n",
       "3              NaN                Andorra  42.506300   1.521800        0   \n",
       "4              NaN                 Angola -11.202700  17.873900        0   \n",
       "..             ...                    ...        ...        ...      ...   \n",
       "248            NaN  Sao Tome and Principe   0.186400   6.613100        0   \n",
       "249            NaN                  Yemen  15.552727  48.516388        0   \n",
       "250            NaN                Comoros -11.645500  43.333300        0   \n",
       "251            NaN             Tajikistan  38.861000  71.276100        0   \n",
       "252            NaN                Lesotho -29.610000  28.233600        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  2007/4/20  2007/5/20  \\\n",
       "0          0        0        0        0        0  ...      19164      19366   \n",
       "1          0        0        0        0        0  ...       1637       1657   \n",
       "2          0        0        0        0        0  ...      11181      11492   \n",
       "3          0        0        0        0        0  ...        800        800   \n",
       "4          0        0        0        0        0  ...        108        108   \n",
       "..       ...      ...      ...      ...      ...  ...        ...        ...   \n",
       "248        0        0        0        0        0  ...        267        268   \n",
       "249        0        0        0        0        0  ...        537        552   \n",
       "250        0        0        0        0        0  ...        241        266   \n",
       "251        0        0        0        0        0  ...       4809       4858   \n",
       "252        0        0        0        0        0  ...         11         11   \n",
       "\n",
       "     2007/6/20  2007/7/20  2007/8/20  2007/9/20  7/13/20  7/14/20  7/15/20  \\\n",
       "0        20103      20179      20700      20847    21254    21454    22456   \n",
       "1         1702       1744       1791       1832     2014     2062     2091   \n",
       "2        11884      12094      12329      12637    14019    14295    14792   \n",
       "3          800        800        802        802      803      803      803   \n",
       "4          108        117        117        117      118      118      124   \n",
       "..         ...        ...        ...        ...      ...      ...      ...   \n",
       "248        269        279        283        283      286      286      322   \n",
       "249        575        591        595        619      675      685      694   \n",
       "250        266        266        272        272      296      302      302   \n",
       "251       4914       4965       5011       5067     5278     5332     5383   \n",
       "252         11         11         11         20       33       48       48   \n",
       "\n",
       "     7/16/20  \n",
       "0      22824  \n",
       "1       2137  \n",
       "2      15107  \n",
       "3        803  \n",
       "4        124  \n",
       "..       ...  \n",
       "248      325  \n",
       "249      695  \n",
       "250      311  \n",
       "251     5431  \n",
       "252       48  \n",
       "\n",
       "[253 rows x 163 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_全球治愈数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>body</th>\n",
       "      <th>id</th>\n",
       "      <th>mainSummary</th>\n",
       "      <th>rumorType</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e33f94b0c6cbdd5bca66176</td>\n",
       "      <td>与宠物接触后，用肥皂和水洗手可以显著减少其他常见细菌在宠物和人类之间的传播，如大肠杆菌和沙门...</td>\n",
       "      <td>5</td>\n",
       "      <td>世界卫生组织回应：目前没有证据显示狗猫等宠物会感染新型冠状病毒</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>人会传染宠物？宠物染病会传染人？</td>\n",
       "      <td>2020-01-31 17:54:18.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5e33f8c50c6cbdd5bca66174</td>\n",
       "      <td>世界卫生组织回应：「目前没有证据显示狗猫等宠物会感染新型冠状病毒。与宠物接触后，用肥皂和水洗...</td>\n",
       "      <td>5</td>\n",
       "      <td>世界卫生组织回应：目前没有证据显示狗猫等宠物会感染新型冠状病毒</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>人会传染宠物？宠物染病会传染人？</td>\n",
       "      <td>2020-01-31 17:52:05.366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5e33d2030c6cbdd5bca66147</td>\n",
       "      <td>青海、南宁、丽江等各城市版本的均为编造。另外，此次新型冠状病毒来源是非法销售的野生动物，目前...</td>\n",
       "      <td>84</td>\n",
       "      <td>丁香医生团队查证：青海、南宁、丽江等各城市版本的均为编造。正规渠道售卖的仍可放心吃。</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>别吃鱼，某地有121个鱼塘感染？</td>\n",
       "      <td>2020-01-31 15:06:42.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5e33d1750c6cbdd5bca66146</td>\n",
       "      <td>目前尚无证据证明喝茶可以预防新冠病毒。尽量保持室内空气流通，注意卫生，勤洗手，吃熟食，远离人...</td>\n",
       "      <td>93</td>\n",
       "      <td>丁香医生团队辟谣：补水挺好，预防病毒没用</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>喝茶可以预防新冠病毒？</td>\n",
       "      <td>2020-01-31 15:04:20.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5e33cc780c6cbdd5bca66145</td>\n",
       "      <td>在国家卫健委印发的《新型冠状病毒感染的肺炎诊疗方案（试行第四版）》中显示，新型冠状病毒经呼吸...</td>\n",
       "      <td>92</td>\n",
       "      <td>丁香医生团队辟谣：系编造，造谣网民已被依法处罚。</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>新冠病毒通过眼神对视传播？</td>\n",
       "      <td>2020-01-31 14:43:03.296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>5e3065acf8be3cb05ba2bf12</td>\n",
       "      <td>1月26日下午从深圳市新型冠状病毒感染的肺炎疫情防控工作领导小组办公室获悉，有关深圳市将要“...</td>\n",
       "      <td>48</td>\n",
       "      <td>深圳市互联网信息办公室：是谣言</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳要封城？</td>\n",
       "      <td>2020-01-29 00:47:32.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>5e3065acf8be3cb05ba2bf13</td>\n",
       "      <td>27日下午，网上流传一张日本媒体报道武汉肺炎疫情的截图，配文称“日本派了1000名医疗工作者...</td>\n",
       "      <td>49</td>\n",
       "      <td>其实是误解</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>日本派出千名医疗人员？</td>\n",
       "      <td>2020-01-29 00:47:32.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>5e3065acf8be3cb05ba2bf14</td>\n",
       "      <td>呼吸阀的气流是单向向外的，不影响使用者的防护效果。但已经有症状的人，建议不要使用带有呼吸阀的...</td>\n",
       "      <td>50</td>\n",
       "      <td>丁香医生团队辟谣：口罩上有呼吸阀不用担心</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>带呼吸阀的口罩没有用？</td>\n",
       "      <td>2020-01-29 00:47:32.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>5e3065acf8be3cb05ba2bf15</td>\n",
       "      <td>一瓶醋里的醋酸含量最多也只有5%。将醋里的醋酸蒸发到空气中的做法，提升空气酸度的空间十分有限...</td>\n",
       "      <td>51</td>\n",
       "      <td>丁香医生团队辟谣：熏醋杀不死新型冠状病毒</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>熏醋可以预防新冠？</td>\n",
       "      <td>2020-01-29 00:47:32.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>5e3065acf8be3cb05ba2bf16</td>\n",
       "      <td>27日下午，一则消息称，自2020年1月28日24时起，市区网约车，出租车一律停止运营。东方...</td>\n",
       "      <td>52</td>\n",
       "      <td>多个平台回复：不实消息</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>上海出租车网约车停运？</td>\n",
       "      <td>2020-01-29 00:47:32.346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174 rows × 9 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                          _id  \\\n",
       "0    5e33f94b0c6cbdd5bca66176   \n",
       "1    5e33f8c50c6cbdd5bca66174   \n",
       "2    5e33d2030c6cbdd5bca66147   \n",
       "3    5e33d1750c6cbdd5bca66146   \n",
       "4    5e33cc780c6cbdd5bca66145   \n",
       "..                        ...   \n",
       "169  5e3065acf8be3cb05ba2bf12   \n",
       "170  5e3065acf8be3cb05ba2bf13   \n",
       "171  5e3065acf8be3cb05ba2bf14   \n",
       "172  5e3065acf8be3cb05ba2bf15   \n",
       "173  5e3065acf8be3cb05ba2bf16   \n",
       "\n",
       "                                                  body  id  \\\n",
       "0    与宠物接触后，用肥皂和水洗手可以显著减少其他常见细菌在宠物和人类之间的传播，如大肠杆菌和沙门...   5   \n",
       "1    世界卫生组织回应：「目前没有证据显示狗猫等宠物会感染新型冠状病毒。与宠物接触后，用肥皂和水洗...   5   \n",
       "2    青海、南宁、丽江等各城市版本的均为编造。另外，此次新型冠状病毒来源是非法销售的野生动物，目前...  84   \n",
       "3    目前尚无证据证明喝茶可以预防新冠病毒。尽量保持室内空气流通，注意卫生，勤洗手，吃熟食，远离人...  93   \n",
       "4    在国家卫健委印发的《新型冠状病毒感染的肺炎诊疗方案（试行第四版）》中显示，新型冠状病毒经呼吸...  92   \n",
       "..                                                 ...  ..   \n",
       "169  1月26日下午从深圳市新型冠状病毒感染的肺炎疫情防控工作领导小组办公室获悉，有关深圳市将要“...  48   \n",
       "170  27日下午，网上流传一张日本媒体报道武汉肺炎疫情的截图，配文称“日本派了1000名医疗工作者...  49   \n",
       "171  呼吸阀的气流是单向向外的，不影响使用者的防护效果。但已经有症状的人，建议不要使用带有呼吸阀的...  50   \n",
       "172  一瓶醋里的醋酸含量最多也只有5%。将醋里的醋酸蒸发到空气中的做法，提升空气酸度的空间十分有限...  51   \n",
       "173  27日下午，一则消息称，自2020年1月28日24时起，市区网约车，出租车一律停止运营。东方...  52   \n",
       "\n",
       "                                    mainSummary  rumorType sourceUrl  summary  \\\n",
       "0               世界卫生组织回应：目前没有证据显示狗猫等宠物会感染新型冠状病毒          2       NaN      NaN   \n",
       "1               世界卫生组织回应：目前没有证据显示狗猫等宠物会感染新型冠状病毒          2       NaN      NaN   \n",
       "2    丁香医生团队查证：青海、南宁、丽江等各城市版本的均为编造。正规渠道售卖的仍可放心吃。          0       NaN      NaN   \n",
       "3                          丁香医生团队辟谣：补水挺好，预防病毒没用          0       NaN      NaN   \n",
       "4                      丁香医生团队辟谣：系编造，造谣网民已被依法处罚。          0       NaN      NaN   \n",
       "..                                          ...        ...       ...      ...   \n",
       "169                             深圳市互联网信息办公室：是谣言          0       NaN      NaN   \n",
       "170                                       其实是误解          0       NaN      NaN   \n",
       "171                        丁香医生团队辟谣：口罩上有呼吸阀不用担心          0       NaN      NaN   \n",
       "172                        丁香医生团队辟谣：熏醋杀不死新型冠状病毒          0       NaN      NaN   \n",
       "173                                 多个平台回复：不实消息          0       NaN      NaN   \n",
       "\n",
       "                title                crawlTime  \n",
       "0    人会传染宠物？宠物染病会传染人？  2020-01-31 17:54:18.985  \n",
       "1    人会传染宠物？宠物染病会传染人？  2020-01-31 17:52:05.366  \n",
       "2    别吃鱼，某地有121个鱼塘感染？  2020-01-31 15:06:42.875  \n",
       "3         喝茶可以预防新冠病毒？  2020-01-31 15:04:20.294  \n",
       "4       新冠病毒通过眼神对视传播？  2020-01-31 14:43:03.296  \n",
       "..                ...                      ...  \n",
       "169            深圳要封城？  2020-01-29 00:47:32.346  \n",
       "170       日本派出千名医疗人员？  2020-01-29 00:47:32.346  \n",
       "171       带呼吸阀的口罩没有用？  2020-01-29 00:47:32.346  \n",
       "172         熏醋可以预防新冠？  2020-01-29 00:47:32.346  \n",
       "173       上海出租车网约车停运？  2020-01-29 00:47:32.346  \n",
       "\n",
       "[174 rows x 9 columns]"
      ]
     },
     "execution_count": 56,
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    "df_新闻"
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       "      <td>56</td>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>79</td>\n",
       "      <td>82</td>\n",
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       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>84070020</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
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       "      <td>Weber-Morgan</td>\n",
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       "      <td>-111.914512</td>\n",
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       "      <td>1293</td>\n",
       "      <td>1368</td>\n",
       "      <td>1396</td>\n",
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       "      <td>1494</td>\n",
       "      <td>1558</td>\n",
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       "      <td>1608</td>\n",
       "      <td>1631</td>\n",
       "      <td>1829</td>\n",
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      "text/plain": [
       "           UID iso2 iso3  code3  FIPS          Admin2  \\\n",
       "0           16   AS  ASM     16  60.0             NaN   \n",
       "1          316   GU  GUM    316  66.0             NaN   \n",
       "2          580   MP  MNP    580  69.0             NaN   \n",
       "3          630   PR  PRI    630  72.0             NaN   \n",
       "4          850   VI  VIR    850  78.0             NaN   \n",
       "...        ...  ...  ...    ...   ...             ...   \n",
       "3256  84070016   US  USA    840   NaN    Central Utah   \n",
       "3257  84070017   US  USA    840   NaN  Southeast Utah   \n",
       "3258  84070018   US  USA    840   NaN  Southwest Utah   \n",
       "3259  84070019   US  USA    840   NaN       TriCounty   \n",
       "3260  84070020   US  USA    840   NaN    Weber-Morgan   \n",
       "\n",
       "                Province_State Country_Region        Lat       Long_  ...  \\\n",
       "0               American Samoa             US -14.271000 -170.132000  ...   \n",
       "1                         Guam             US  13.444300  144.793700  ...   \n",
       "2     Northern Mariana Islands             US  15.097900  145.673900  ...   \n",
       "3                  Puerto Rico             US  18.220800  -66.590100  ...   \n",
       "4               Virgin Islands             US  18.335800  -64.896300  ...   \n",
       "...                        ...            ...        ...         ...  ...   \n",
       "3256                      Utah             US  39.372319 -111.575868  ...   \n",
       "3257                      Utah             US  38.996171 -110.701396  ...   \n",
       "3258                      Utah             US  37.854472 -111.441876  ...   \n",
       "3259                      Utah             US  40.124915 -109.517442  ...   \n",
       "3260                      Utah             US  41.271160 -111.914512  ...   \n",
       "\n",
       "     7/7/20  7/8/20  7/9/20  7/10/20  7/11/20  7/12/20  7/13/20  7/14/20  \\\n",
       "0         0       0       0        0        0        0        0        0   \n",
       "1       303     307     309      310      310      310      312      312   \n",
       "2        31      31      31       31       33       33       33       36   \n",
       "3      8714    8745    8862     9137     9366     9654    10010    10123   \n",
       "4       116     122     144      153      167      181      206      206   \n",
       "...     ...     ...     ...      ...      ...      ...      ...      ...   \n",
       "3256    227     234     243      263      266      270      271      273   \n",
       "3257     41      45      47       50       54       54       55       54   \n",
       "3258   1805    1840    1881     1949     2005     2069     2116     2140   \n",
       "3259     56      59      63       63       65       69       72       73   \n",
       "3260   1293    1368    1396     1456     1494     1558     1580     1608   \n",
       "\n",
       "      7/15/20  7/16/20  \n",
       "0           0        0  \n",
       "1         313      314  \n",
       "2          36       37  \n",
       "3       10379    10574  \n",
       "4         243      249  \n",
       "...       ...      ...  \n",
       "3256      278      284  \n",
       "3257       54       54  \n",
       "3258     2158     2239  \n",
       "3259       79       82  \n",
       "3260     1631     1829  \n",
       "\n",
       "[3261 rows x 188 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
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   ],
   "source": [
    "df_美国确诊数"
   ]
  },
  {
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   "execution_count": 58,
   "metadata": {},
   "outputs": [
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       "      <td>PR</td>\n",
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       "      <td>-66.590100</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Central Utah</td>\n",
       "      <td>Utah</td>\n",
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       "      <td>Southeast Utah</td>\n",
       "      <td>Utah</td>\n",
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       "      <td>-110.701396</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>84070018</td>\n",
       "      <td>US</td>\n",
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       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southwest Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>US</td>\n",
       "      <td>37.854472</td>\n",
       "      <td>-111.441876</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>84070019</td>\n",
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       "      <td>USA</td>\n",
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       "      <td>-109.517442</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>84070020</td>\n",
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       "      <td>-111.914512</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
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       "      <td>18</td>\n",
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       "<p>3261 rows × 189 columns</p>\n",
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       "           UID iso2 iso3  code3  FIPS          Admin2  \\\n",
       "0           16   AS  ASM     16  60.0             NaN   \n",
       "1          316   GU  GUM    316  66.0             NaN   \n",
       "2          580   MP  MNP    580  69.0             NaN   \n",
       "3          630   PR  PRI    630  72.0             NaN   \n",
       "4          850   VI  VIR    850  78.0             NaN   \n",
       "...        ...  ...  ...    ...   ...             ...   \n",
       "3256  84070016   US  USA    840   NaN    Central Utah   \n",
       "3257  84070017   US  USA    840   NaN  Southeast Utah   \n",
       "3258  84070018   US  USA    840   NaN  Southwest Utah   \n",
       "3259  84070019   US  USA    840   NaN       TriCounty   \n",
       "3260  84070020   US  USA    840   NaN    Weber-Morgan   \n",
       "\n",
       "                Province_State Country_Region        Lat       Long_  ...  \\\n",
       "0               American Samoa             US -14.271000 -170.132000  ...   \n",
       "1                         Guam             US  13.444300  144.793700  ...   \n",
       "2     Northern Mariana Islands             US  15.097900  145.673900  ...   \n",
       "3                  Puerto Rico             US  18.220800  -66.590100  ...   \n",
       "4               Virgin Islands             US  18.335800  -64.896300  ...   \n",
       "...                        ...            ...        ...         ...  ...   \n",
       "3256                      Utah             US  39.372319 -111.575868  ...   \n",
       "3257                      Utah             US  38.996171 -110.701396  ...   \n",
       "3258                      Utah             US  37.854472 -111.441876  ...   \n",
       "3259                      Utah             US  40.124915 -109.517442  ...   \n",
       "3260                      Utah             US  41.271160 -111.914512  ...   \n",
       "\n",
       "     7/7/20  7/8/20  7/9/20  7/10/20  7/11/20  7/12/20  7/13/20  7/14/20  \\\n",
       "0         0       0       0        0        0        0        0        0   \n",
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       "3260       18       18  \n",
       "\n",
       "[3261 rows x 189 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_美国死亡数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
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      "text/plain": [
       "       Sno                 Date   Province/State Country          Last Update  \\\n",
       "0        1  01/22/2020 12:00:00            Anhui   China  01/22/2020 12:00:00   \n",
       "1        2  01/22/2020 12:00:00          Beijing   China  01/22/2020 12:00:00   \n",
       "2        3  01/22/2020 12:00:00        Chongqing   China  01/22/2020 12:00:00   \n",
       "3        4  01/22/2020 12:00:00           Fujian   China  01/22/2020 12:00:00   \n",
       "4        5  01/22/2020 12:00:00            Gansu   China  01/22/2020 12:00:00   \n",
       "...    ...                  ...              ...     ...                  ...   \n",
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       "1716  1717  02/17/2020 22:00:00  San Antonio, TX      US  2020-02-13T18:53:02   \n",
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       "1718  1719  02/17/2020 22:00:00        Tempe, AZ      US  2020-02-01T19:43:03   \n",
       "\n",
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     },
     "execution_count": 59,
     "metadata": {},
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   ],
   "source": [
    "df_全球总体"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对分类的数据进行整合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
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       "      <td>22456</td>\n",
       "      <td>22824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1637</td>\n",
       "      <td>1657</td>\n",
       "      <td>1702</td>\n",
       "      <td>1744</td>\n",
       "      <td>1791</td>\n",
       "      <td>1832</td>\n",
       "      <td>2014</td>\n",
       "      <td>2062</td>\n",
       "      <td>2091</td>\n",
       "      <td>2137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11181</td>\n",
       "      <td>11492</td>\n",
       "      <td>11884</td>\n",
       "      <td>12094</td>\n",
       "      <td>12329</td>\n",
       "      <td>12637</td>\n",
       "      <td>14019</td>\n",
       "      <td>14295</td>\n",
       "      <td>14792</td>\n",
       "      <td>15107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>802</td>\n",
       "      <td>802</td>\n",
       "      <td>803</td>\n",
       "      <td>803</td>\n",
       "      <td>803</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>24.21550</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.18640</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>267</td>\n",
       "      <td>268</td>\n",
       "      <td>269</td>\n",
       "      <td>279</td>\n",
       "      <td>283</td>\n",
       "      <td>283</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>322</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.64550</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>241</td>\n",
       "      <td>266</td>\n",
       "      <td>266</td>\n",
       "      <td>266</td>\n",
       "      <td>272</td>\n",
       "      <td>272</td>\n",
       "      <td>296</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.86100</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4809</td>\n",
       "      <td>4858</td>\n",
       "      <td>4914</td>\n",
       "      <td>4965</td>\n",
       "      <td>5011</td>\n",
       "      <td>5067</td>\n",
       "      <td>5278</td>\n",
       "      <td>5332</td>\n",
       "      <td>5383</td>\n",
       "      <td>5431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-29.61000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 499 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State         Country/Region       Lat       Long  1/22/20_x  \\\n",
       "0              NaN            Afghanistan  33.93911  67.709953          0   \n",
       "1              NaN                Albania  41.15330  20.168300          0   \n",
       "2              NaN                Algeria  28.03390   1.659600          0   \n",
       "3              NaN                Andorra  42.50630   1.521800          0   \n",
       "4              NaN                 Angola -11.20270  17.873900          0   \n",
       "..             ...                    ...       ...        ...        ...   \n",
       "212            NaN         Western Sahara  24.21550 -12.885800          0   \n",
       "213            NaN  Sao Tome and Principe   0.18640   6.613100          0   \n",
       "214            NaN                Comoros -11.64550  43.333300          0   \n",
       "215            NaN             Tajikistan  38.86100  71.276100          0   \n",
       "216            NaN                Lesotho -29.61000  28.233600          0   \n",
       "\n",
       "     1/23/20_x  1/24/20_x  1/25/20_x  1/26/20_x  1/27/20_x  ...  2007/4/20_y  \\\n",
       "0            0          0          0          0          0  ...        19164   \n",
       "1            0          0          0          0          0  ...         1637   \n",
       "2            0          0          0          0          0  ...        11181   \n",
       "3            0          0          0          0          0  ...          800   \n",
       "4            0          0          0          0          0  ...          108   \n",
       "..         ...        ...        ...        ...        ...  ...          ...   \n",
       "212          0          0          0          0          0  ...            8   \n",
       "213          0          0          0          0          0  ...          267   \n",
       "214          0          0          0          0          0  ...          241   \n",
       "215          0          0          0          0          0  ...         4809   \n",
       "216          0          0          0          0          0  ...           11   \n",
       "\n",
       "     2007/5/20_y  2007/6/20_y  2007/7/20_y  2007/8/20_y  2007/9/20_y  7/13/20  \\\n",
       "0          19366        20103        20179        20700        20847    21254   \n",
       "1           1657         1702         1744         1791         1832     2014   \n",
       "2          11492        11884        12094        12329        12637    14019   \n",
       "3            800          800          800          802          802      803   \n",
       "4            108          108          117          117          117      118   \n",
       "..           ...          ...          ...          ...          ...      ...   \n",
       "212            8            8            8            8            8        8   \n",
       "213          268          269          279          283          283      286   \n",
       "214          266          266          266          272          272      296   \n",
       "215         4858         4914         4965         5011         5067     5278   \n",
       "216           11           11           11           11           20       33   \n",
       "\n",
       "     7/14/20  7/15/20  7/16/20  \n",
       "0      21454    22456    22824  \n",
       "1       2062     2091     2137  \n",
       "2      14295    14792    15107  \n",
       "3        803      803      803  \n",
       "4        118      124      124  \n",
       "..       ...      ...      ...  \n",
       "212        8        8        8  \n",
       "213      286      322      325  \n",
       "214      302      302      311  \n",
       "215     5332     5383     5431  \n",
       "216       48       48       48  \n",
       "\n",
       "[217 rows x 499 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "全球疫情 = pd.merge(df_全球死亡数, df_全球确诊数, on=['Province/State','Country/Region','Lat','Long'])\n",
    "全球疫情 = pd.merge(全球疫情, df_全球治愈数, on=['Province/State','Country/Region','Lat','Long'])\n",
    "全球疫情"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择包含全球“确诊”、“死亡”、“治愈”的数据进行可视化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset= pd.read_csv(\"./data/新冠状病毒数据/全球总体.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1719 entries, 0 to 1718\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Sno             1719 non-null   int64  \n",
      " 1   Date            1719 non-null   object \n",
      " 2   Province/State  1257 non-null   object \n",
      " 3   Country         1719 non-null   object \n",
      " 4   Last Update     1719 non-null   object \n",
      " 5   Confirmed       1719 non-null   float64\n",
      " 6   Deaths          1719 non-null   float64\n",
      " 7   Recovered       1719 non-null   float64\n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 107.6+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>Date</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>01/23/2020 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>01/23/2020 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>01/23/2020 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>01/23/2020 12:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>1707</td>\n",
       "      <td>02/17/2020 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>2020-02-14T23:53:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>1708</td>\n",
       "      <td>02/17/2020 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finland</td>\n",
       "      <td>2020-02-12T00:03:12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709</th>\n",
       "      <td>1710</td>\n",
       "      <td>02/17/2020 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>2020-02-12T14:43:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710</th>\n",
       "      <td>1711</td>\n",
       "      <td>02/17/2020 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>2020-02-08T03:43:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>1712</td>\n",
       "      <td>02/17/2020 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2020-02-01T02:13:26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sno                 Date Province/State      Country  \\\n",
       "35      36  01/22/2020 12:00:00            NaN        Japan   \n",
       "36      37  01/22/2020 12:00:00            NaN     Thailand   \n",
       "37      38  01/22/2020 12:00:00            NaN  South Korea   \n",
       "73      74  01/23/2020 12:00:00            NaN        Japan   \n",
       "74      75  01/23/2020 12:00:00            NaN     Thailand   \n",
       "...    ...                  ...            ...          ...   \n",
       "1706  1707  02/17/2020 22:00:00            NaN        Egypt   \n",
       "1707  1708  02/17/2020 22:00:00            NaN      Finland   \n",
       "1709  1710  02/17/2020 22:00:00            NaN        Nepal   \n",
       "1710  1711  02/17/2020 22:00:00            NaN    Sri Lanka   \n",
       "1711  1712  02/17/2020 22:00:00            NaN       Sweden   \n",
       "\n",
       "              Last Update  Confirmed  Deaths  Recovered  \n",
       "35    01/22/2020 12:00:00        2.0     0.0        0.0  \n",
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\"domain\": [0.0, 1.0], \"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Confirmed\"}}},\n",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = px.bar(dataset, x='Date', y='Confirmed', hover_data=['Province/State', 'Deaths', 'Recovered'], color='Country')\n",
    "annotations = []\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
    "                              xanchor='left', yanchor='bottom',\n",
    "                              text='全球各国疫情的“确诊”、“死亡”、“治愈”比例',\n",
    "                              font=dict(family='Arial',\n",
    "                                        size=30,\n",
    "                                        color='rgb(37,37,37)'),\n",
    "                              showarrow=False))\n",
    "fig.update_layout(annotations=annotations)\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Confirmed\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('c7efdf67-4b40-4141-a0e9-36758a9b5818');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(dataset, x=\"Date\", y=\"Confirmed\", color='Country', hover_data=['Province/State', 'Deaths'])\n",
    "annotations = []\n",
    "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
    "                              xanchor='left', yanchor='bottom',\n",
    "                              text='每个国家的确诊人数',\n",
    "                              font=dict(family='Arial',\n",
    "                                        size=30,\n",
    "                                        color='rgb(37,37,37)'),\n",
    "                              showarrow=False))\n",
    "fig.update_layout(annotations=annotations)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将数据聚合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/23/2020 12:00:00</td>\n",
       "      <td>653.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/24/2020 12:00:00</td>\n",
       "      <td>941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/25/2020 22:00:00</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/26/2020 23:00:00</td>\n",
       "      <td>2794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01/27/2020 20:30:00</td>\n",
       "      <td>4473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01/28/2020 23:00:00</td>\n",
       "      <td>6057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01/29/2020 21:00:00</td>\n",
       "      <td>7783.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01/30/2020 21:30:00</td>\n",
       "      <td>9776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01/31/2020 19:00:00</td>\n",
       "      <td>11374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>02/01/2020 23:00:00</td>\n",
       "      <td>14549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>02/02/2020 21:00:00</td>\n",
       "      <td>17295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>02/03/2020 21:40:00</td>\n",
       "      <td>20588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>02/04/2020 22:00:00</td>\n",
       "      <td>24503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>02/05/2020 12:20:00</td>\n",
       "      <td>24630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>02/06/2020 20:05:00</td>\n",
       "      <td>30806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>02/07/2020 20:24:00</td>\n",
       "      <td>31532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>02/08/2020 23:04:00</td>\n",
       "      <td>37549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>02/09/2020 23:20:00</td>\n",
       "      <td>40536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>02/10/2020 19:30:00</td>\n",
       "      <td>42767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>02/11/2020 20:44:00</td>\n",
       "      <td>45117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>02/12/2020 22:00:00</td>\n",
       "      <td>60328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>02/13/2020 21:15:00</td>\n",
       "      <td>64422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>02/14/2020 22:00:00</td>\n",
       "      <td>66887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>02/15/2020 22:00:00</td>\n",
       "      <td>69032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>02/16/2020 22:00:00</td>\n",
       "      <td>71226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>02/17/2020 22:00:00</td>\n",
       "      <td>73260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ds        y\n",
       "0   01/22/2020 12:00:00    555.0\n",
       "1   01/23/2020 12:00:00    653.0\n",
       "2   01/24/2020 12:00:00    941.0\n",
       "3   01/25/2020 22:00:00   2019.0\n",
       "4   01/26/2020 23:00:00   2794.0\n",
       "5   01/27/2020 20:30:00   4473.0\n",
       "6   01/28/2020 23:00:00   6057.0\n",
       "7   01/29/2020 21:00:00   7783.0\n",
       "8   01/30/2020 21:30:00   9776.0\n",
       "9   01/31/2020 19:00:00  11374.0\n",
       "10  02/01/2020 23:00:00  14549.0\n",
       "11  02/02/2020 21:00:00  17295.0\n",
       "12  02/03/2020 21:40:00  20588.0\n",
       "13  02/04/2020 22:00:00  24503.0\n",
       "14  02/05/2020 12:20:00  24630.0\n",
       "15  02/06/2020 20:05:00  30806.0\n",
       "16  02/07/2020 20:24:00  31532.0\n",
       "17  02/08/2020 23:04:00  37549.0\n",
       "18  02/09/2020 23:20:00  40536.0\n",
       "19  02/10/2020 19:30:00  42767.0\n",
       "20  02/11/2020 20:44:00  45117.0\n",
       "21  02/12/2020 22:00:00  60328.0\n",
       "22  02/13/2020 21:15:00  64422.0\n",
       "23  02/14/2020 22:00:00  66887.0\n",
       "24  02/15/2020 22:00:00  69032.0\n",
       "25  02/16/2020 22:00:00  71226.0\n",
       "26  02/17/2020 22:00:00  73260.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_training_dataset = pd.DataFrame(dataset.groupby('Date')['Confirmed'].sum().reset_index()).rename(columns={'Date': 'ds', 'Confirmed': 'y'})\n",
    "confirmed_training_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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